import argparse
import logging
import sys
from copy import deepcopy

sys.path.append('./')  # to run '$ python *.py' files in subdirectories
logger = logging.getLogger(__name__)

from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import make_divisible, check_file, set_logging
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
    select_device, copy_attr

try:
    import thop  # for FLOPS computation
except ImportError:
    thop = None


class Detect(nn.Module):#Detect层！！！！！！！！！！！
    stride = None  # strides computed during build
    export = False  # onnx export

    #nc :number of class类别数  anchors三个anchors   ch:channel输入的通道数list
    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor #每个anchor输出值的个数，+5是指4个坐标+置信度
        self.nl = len(anchors)  # number of detection layers #做检测特征图层的个数  几个grid预测anchor
        self.na = len(anchors[0]) // 2  # number of anchors #anchor的个数 一个grid里面有3个anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid 初始化grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)#将
        #模型中需要保存下来的参数有两种，一种是反向传播需要被optimizer更新的，称为parameter;
        #另一种不需要被optimizer更新的，称为buffer
        """
            对于第二种参数我们需要创建tensor,然后将tensor通过register_buffer进行注册
            可以通过model.buffer返回，注册完后参数也会自动保存到orderdict中去
            注意：buffer的更新在forward里，optim.step只更新nn.parameter类型的参数
        """
        self.register_buffer('anchors', a)  # shape(nl,na,2)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv #非常关键的一步！！！！ 把输入的channel转换为anchor的个数*单个框的输出

    def forward(self, x): #输入[torch.Size([20, 192, 52, 84]),torch.Size([20, 384, 26, 42]),torch.Size([20, 768, 13, 21])]
        # x = x.copy()  # for profiling
        z = []  # inference output
        self.training |= self.export
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85) bs:batch size
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()#调整顺序

            if not self.training:  # inference #如果没有训练的话要做额外的工作，也就是调用make grid构造网格
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()#回归方程！！！！！！！！！！！！！！！！
                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))#预测框的信息
        #z是list torch.cat将他们拼接
        return x if self.training else (torch.cat(z, 1), x) #训练和不是训练模式恢复的数据不一样 x:【torch.Size([20, 3, 52, 84, 8])，torch.Size([20, 3, 26, 42, 8])，torch.Size([20, 3, 13, 21, 8])】

    @staticmethod #划分单位网格，划分nx,ny
    def _make_grid(nx=20, ny=20):
        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()


class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
        super(Model, self).__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml #读取配置文件
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            with open(cfg) as f:
                self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels 得到图的输入的channel层 赋值给self.yaml['ch']
        if nc and nc != self.yaml['nc']:
            logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc))
            self.yaml['nc'] = nc  # override yaml value
        """
            model里面也包含detect
            ('24', Detect(
              (m): ModuleList(
                (0): Conv2d(192, 24, kernel_size=(1, 1), stride=(1, 1))
                (1): Conv2d(384, 24, kernel_size=(1, 1), stride=(1, 1))
                (2): Conv2d(768, 24, kernel_size=(1, 1), stride=(1, 1))
              )
        """
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist  解析model模块！！！！！！！！！！！
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect): #如果m是detect的实例就为true
            s = 256  # 2x min stride
            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward #[8,16,32] 下采样倍率？
            #anchor大小计算，例如[10,13]->[1.25,1.625]
            """
                tensor([[[ 14.,  27.],
                         [ 23.,  46.],
                         [ 28., 130.]],
                
                        [[ 39., 148.],-----》
                         [ 52., 186.],
                         [ 62., 279.]],
                
                        [[ 85., 237.],
                         [ 88., 360.],
                         [145., 514.]]])
 
                tensor([[[ 1.75000,  3.37500],
                         [ 2.87500,  5.75000],
                         [ 3.50000, 16.25000]],
                
                        [[ 2.43750,  9.25000],
                         [ 3.25000, 11.62500],
                         [ 3.87500, 17.43750]],
                
                        [[ 2.65625,  7.40625],
                         [ 2.75000, 11.25000],
                         [ 4.53125, 16.06250]]])    
            """
            m.anchors /= m.stride.view(-1, 1, 1) #view是将其reshape
            check_anchor_order(m)#检查anchor和stride顺序是否一致
            self.stride = m.stride
            self._initialize_biases()  # only run once 初始化偏置(detect中使用） 卷积核不使用bias，这主要是因为使用了BN层，就没有必要再用bias了。
            # print('Strides: %s' % m.stride.tolist())

        # Init weights, biases
        initialize_weights(self) #初始化权重 batchnormalization和激活函数  所以再次训练得要用resume!!!
        # [W NNPACK.cpp:80] Could not initialize NNPACK! Reason: Unsupported hardware.
        # Model Summary: 391 layers, 21493896 parameters, 21493896 gradients, 51.4 GFLOPS
        self.info()  #打印网络层信息
        logger.info('')

    def forward(self, x, augment=False, profile=False):#默认是false值可以暂时跳过
        if augment: ##如果有--augment  没有就忽略  （用于TTA[TEST time augmentation]）
            img_size = x.shape[-2:]  # height, width
            s = [1, 0.83, 0.67]  # scales
            f = [None, 3, None]  # flips (2-ud, 3-lr)
            y = []  # outputs
            for si, fi in zip(s, f):
                xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
                yi = self.forward_once(xi)[0]  # forward
                # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
                yi[..., :4] /= si  # de-scale
                if fi == 2:
                    yi[..., 1] = img_size[0] - 1 - yi[..., 1]  # de-flip ud
                elif fi == 3:
                    yi[..., 0] = img_size[1] - 1 - yi[..., 0]  # de-flip lr
                y.append(yi)
            return torch.cat(y, 1), None  # augmented inference, train
        else:
            return self.forward_once(x, profile)  # single-scale inference, train

    def forward_once(self, x, profile=False): #一步步怎么得到的最终结果  训练过程
        """
        层数：10,输出卷积图大小:torch.Size([1, 768, 8, 8])
        层数：11,输入卷积图大小:torch.Size([1, 384, 8, 8])
        层数：12,输入卷积图大小:torch.Size([1, 384, 16, 16])
        层数：13,输入卷积图大小:torch.Size([1, 768, 16, 16])
        层数：14,输入卷积图大小:torch.Size([1, 384, 16, 16])
        层数：15,输入卷积图大小:torch.Size([1, 192, 16, 16])
        层数：16,输入卷积图大小:torch.Size([1, 192, 32, 32])
        层数：17,输入卷积图大小:torch.Size([1, 384, 32, 32])
        :param x:
        :param profile:
        :return:
        """
        y, dt = [], []  # outputs
        i=1
        for m in self.model:#一步步走
            if m.f != -1:  # if not from previous layer #如果不是从上一层取数或者是list?
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers 对于list和单个非-1数的处理

            if profile:
                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
                t = time_synchronized()
                for _ in range(10):
                    _ = m(x)
                dt.append((time_synchronized() - t) * 100)
                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
            x = m(x)  # run 把数据x传入到数据当中 经过每个网络
            # print("层数：{0},输出卷积图大小:{1}".format(i, x.shape))

            i+=1
            y.append(x if m.i in self.save else None)  # save output

        if profile:
            print('%.1fms total' % sum(dt))
        return x

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

    # def _print_weights(self):
    #     for m in self.model.modules():
    #         if type(m) is Bottleneck:
    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers  卷积和批归一化进行融合
        print('Fusing layers... ')
        for m in self.model.modules():
            if type(m) is Conv and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, 'bn')  # remove batchnorm
                m.forward = m.fuseforward  # update forward
        self.info()
        return self

    def nms(self, mode=True):  # add or remove NMS module 用来添加或者删除nms模块
        present = type(self.model[-1]) is NMS  # last layer is NMS
        if mode and not present:
            print('Adding NMS... ')
            m = NMS()  # module
            m.f = -1  # from
            m.i = self.model[-1].i + 1  # index
            self.model.add_module(name='%s' % m.i, module=m)  # add
            self.eval()
        elif not mode and present:
            print('Removing NMS... ')
            self.model = self.model[:-1]  # remove
        return self

    def autoshape(self):  # add autoShape module
        print('Adding autoShape... ')
        m = autoShape(self)  # wrap model
        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
        return m

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)


def parse_model(d, ch):  # model_dict, input_channels(3) #输入d：为读取的yaml文件   ch:为channel数
    logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']#nc为class数目  anchor为加载的anchor二维数组
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5) #(classes + 5)类别加4个坐标值+置信度

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args #【当前层输入是哪里（-1表示上一层），重复次数，模块名，参数（卷积核个数和channel）】
        m = eval(m) if isinstance(m, str) else m  # eval strings 获取该层的class
        for j, a in enumerate(args):#将参数str转化为int
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except:
                pass

        n = max(round(n * gd), 1) if n > 1 else n  # depth gain 当前层的number数小于1就当做1
        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
                 C3]:
            c1, c2 = ch[f], args[0] #c1 输入的channel c2输出的
            if c2 != no:  # if not output 如果不是output不是24 （因为卷积核不一定是这样，因为width_depth）
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]#最后的表示卷积核大小为3x3
            if m in [BottleneckCSP, C3]:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum([ch[x] for x in f])
        elif m is Detect:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            c2 = ch[f]
        #*args表示接受任意个参数，调用时会打包成元组传入
        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        np = sum([x.numel() for x in m_.parameters()])  # number params 计算参数有多少
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        # from  n    params  module                                  arguments  
        #  -1  1      5280  models.common.Focus                     [3, 48, 3]
        logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_) #layers包含了所有的层
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    opt = parser.parse_args()
    opt.cfg = check_file(opt.cfg)  # check file
    set_logging()
    device = select_device(opt.device)

    # Create model
    model = Model(opt.cfg).to(device)
    model.train()

    # Profile
    # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
    # y = model(img, profile=True)

    # Tensorboard
    # from torch.utils.tensorboard import SummaryWriter
    # tb_writer = SummaryWriter()
    # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
    # tb_writer.add_graph(model.model, img)  # add model to tensorboard
    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard
